• An influential node identification method considering multi-attribute decision fusion and dependency

    Item Type Journal Article
    Author Chao-Yang Chen
    Author Dingrong Tan
    Author Xiangyi Meng
    Author Jianxi Gao
    Abstract It is essential to study the robustness and centrality of interdependent networks for building reliable interdependent systems. Here, we consider a nonlinear load-capacity cascading failure model on interdependent networks, where the initial load distribution is not random, as usually assumed, but determined by the influence of each node in the interdependent network. The node influence is measured by an automated entropy-weighted multi-attribute algorithm that takes into account both different centrality measures of nodes and the interdependence of node pairs, then averaging for not only the node itself but also its nearest neighbors and next-nearest neighbors. The resilience of interdependent networks under such a more practical and accurate setting is thoroughly investigated for various network parameters, as well as how nodes from different layers are coupled and the corresponding coupling strength. The results thereby can help better monitoring interdependent systems.
    Date 2022-11-14
    Language en
    Library Catalog www.nature.com
    URL https://www.nature.com/articles/s41598-022-23430-3
    Accessed 11/17/2022, 6:54:39 AM
    Rights 2022 The Author(s)
    Extra Number: 1 Publisher: Nature Publishing Group
    Volume 12
    Pages 19465
    Publication Scientific Reports
    DOI 10.1038/s41598-022-23430-3
    Issue 1
    Journal Abbr Sci Rep
    ISSN 2045-2322
    Date Added 11/17/2022, 6:54:39 AM
    Modified 11/17/2022, 6:54:39 AM

    Tags:

    • Complex networks
    • Nonlinear phenomena

    Notes:

    Attachments

    • ctmg22.pdf

      Contents

      • Results
      • Discussion
      • Methods
        • Cascading failure propagation model for interdependent networks.
        • Node influence identification.
          • Degree centrality.
          • Betweenness centrality.
          • Eigenvector centrality.
          • Cloneseness centrality.
          • K-shell decomposition.
          • Main idea.
          • Computational process.
        • Evaluation indicator.
      • References
      • Acknowledgements
  • Concurrence Percolation in Quantum Networks

    Item Type Journal Article
    Author Xiangyi Meng
    Author Jianxi Gao
    Author Shlomo Havlin
    Abstract Establishing long-distance quantum entanglement, i.e., entanglement transmission, in quantum networks (QN) is a key and timely challenge for developing efficient quantum communication. Traditional comprehension based on classical percolation assumes a necessary condition for successful entanglement transmission between any two infinitely distant nodes: they must be connected by at least a path of perfectly entangled states (singlets). Here, we relax this condition by explicitly showing that one can focus not on optimally converting singlets but on establishing concurrence—a key measure of bipartite entanglement. We thereby introduce a new statistical theory, concurrence percolation theory (ConPT), remotely analogous to classical percolation but fundamentally different, built by generalizing bond percolation in terms of “sponge-crossing” paths instead of clusters. Inspired by resistance network analysis, we determine the path connectivity by series and parallel rules and approximate higher-order rules via star-mesh transforms. Interestingly, we find that the entanglement transmission threshold predicted by ConPT is lower than the known classical-percolation-based results and is readily achievable on any series-parallel networks such as the Bethe lattice. ConPT promotes our understanding of how well quantum communication can be further systematically improved versus classical statistical predictions under the limitation of QN locality—a “quantum advantage” that is more general and efficient than expected. ConPT also shows a percolationlike universal critical behavior derived by finite-size analysis on the Bethe lattice and regular two-dimensional lattices, offering new perspectives for a theory of criticality in entanglement statistics.
    Date 2021-04-27
    Library Catalog APS
    URL https://link.aps.org/doi/10.1103/PhysRevLett.126.170501
    Accessed 4/27/2021, 10:29:05 AM
    Rights All rights reserved
    Extra Citation Key: conpt_mgh21
    Volume 126
    Pages 170501
    Publication Physical Review Letters
    DOI 10.1103/PhysRevLett.126.170501
    Issue 17
    Journal Abbr Phys. Rev. Lett.
    Date Added 4/27/2021, 10:29:05 AM
    Modified 10/21/2022, 5:10:21 PM

    Tags:

    • Quantum network
    • Complexity

    Notes:

    Attachments

    • mgh21_suppl.pdf
    • mgh21.pdf
  • Concurrence percolation threshold of large-scale quantum networks

    Item Type Journal Article
    Author Omar Malik
    Author Xiangyi Meng
    Author Shlomo Havlin
    Author Gyorgy Korniss
    Author Boleslaw Karol Szymanski
    Author Jianxi Gao
    Abstract Quantum networks describe communication networks that are based on quantum entanglement. A concurrence percolation theory has been recently developed to determine the required entanglement to enable communication between two distant stations in an arbitrary quantum network. Unfortunately, concurrence percolation has been calculated only for very small networks or large networks without loops. Here, we develop a set of mathematical tools for approximating the concurrence percolation threshold for unprecedented large-scale quantum networks by estimating the path-length distribution, under the assumption that all paths between a given pair of nodes have no overlap. We show that our approximate method agrees closely with analytical results from concurrence percolation theory. The numerical results we present include 2D square lattices of 2002 nodes and complex networks of up to 104 nodes. The entanglement percolation threshold of a quantum network is a crucial parameter for constructing a real-world communication network based on entanglement, and our method offers a significant speed-up for the intensive computations involved.
    Date 2022-07-29
    Language en
    Library Catalog www.nature.com
    URL https://www.nature.com/articles/s42005-022-00958-4
    Accessed 8/23/2022, 11:21:22 PM
    Rights 2022 The Author(s)
    Extra bibtex:conpt_mmhksg22
    Volume 5
    Pages 1-11
    Publication Communications Physics
    DOI 10.1038/s42005-022-00958-4
    Issue 1
    Journal Abbr Commun. Phys.
    ISSN 2399-3650
    Date Added 8/23/2022, 11:21:22 PM
    Modified 9/21/2022, 9:05:25 PM

    Tags:

    • Quantum network

    Notes:

    Attachments

    • mmhksg22.pdf

      Contents

      • Results and discussion
        • Concurrence percolation theory
        • A fast and tangible solution for concurrence percolation
        • Parallel approximation
        • Sm approximation
        • Bethe Lattice (Cayley Tree)
        • 2D square lattices
        • Piecewise path enumeration algorithm
        • Numerical calculations
        • Complex network topologies
        • Erdős–nobreakRényi Network
        • Barabási–nobreakAlbert network
        • Comparison with Classical Entanglement Percolation (CEP)
      • Conclusion
      • Data availability
      • References
      • References
      • Acknowledgements
      • Author contributions
      • Competing interests
      • Additional information
  • Deterministic entanglement distribution on series-parallel quantum networks

    Item Type Journal Article
    Author Xiangyi Meng
    Author Yulong Cui
    Author Jianxi Gao
    Author Shlomo Havlin
    Author Andrei E. Ruckenstein
    Abstract The performance of distributing entanglement between two distant nodes in a large-scale quantum network (QN) of partially entangled bipartite pure states is generally benchmarked against the classical entanglement percolation (CEP) scheme. Improvements beyond CEP were only achieved by nonscalable strategies for restricted QN topologies. This paper explores and amplifies a new and more effective mapping of a QN, referred to as concurrence percolation theory (ConPT), that suggests using deterministic rather than probabilistic protocols for scalably improving on CEP across arbitrary QN topology. More precisely, we implement ConPT via a deterministic entanglement transmission (DET) scheme that is fully analogous to resistor network analysis, with the corresponding series and parallel rules represented by deterministic entanglement swapping and concentration protocols, respectively. The main contribution of this paper is to establish a powerful mathematical framework, which is applicable to arbitrary d-dimensional information carriers (qudits), that provides different natural optimality metrics in terms of generalized k-concurrences (a family of fundamental entanglement measures) for different QN topologies. In particular, we conclude that the introduced DET scheme (a) is optimal over the well-known nested repeater protocol for distilling entanglement from partially entangled qubits and (b) leads to higher success probabilities of obtaining a maximally entangled state than using CEP. The implementation of the DET scheme is experimentally feasible as tested on IBM's quantum computation platform.
    Date 2023-03-31
    Library Catalog APS
    URL https://link.aps.org/doi/10.1103/PhysRevResearch.5.013225
    Accessed 3/31/2023, 9:51:17 AM
    Rights All rights reserved
    Extra bibtex:det_mcghr23
    Volume 5
    Pages 013225
    Publication Physical Review Research
    DOI 10.1103/PhysRevResearch.5.013225
    Issue 1
    Journal Abbr Phys. Rev. Res.
    Date Added 3/31/2023, 9:51:17 AM
    Modified 8/11/2023, 11:52:28 AM

    Notes:

    Attachments

    • mcghr23.pdf
  • Diffusion interactions between crossing fibers of the brain

    Item Type Journal Article
    Author Sergey V. Buldyrev
    Author Xiangyi Meng
    Author Timothy G. Reese
    Author Farzad Mortazavi
    Author Douglas L. Rosene
    Author H. Eugene Stanley
    Author Van J. Wedeen
    Abstract Purpose Recent observations of several preferred orientations of diffusion in deep white matter may indicate either (a) that axons in different directions are independently bundled in thick sheets and function noninteractively, or more interestingly, (b) that the axons are closely interwoven and would exhibit branching and sharp turns. This study aims to investigate whether the dependence of dMRI Q-ball signal on the interpulse time can decode the smaller-than-voxel-size brain structure, in particular, to distinguish scenarios (a) and (b). Methods High-resolution Q-ball images of a healthy brain taken with s/mm2 for 3 different values of were analyzed. The exchange of water molecules between crossing fibers was characterized by the fourth Fourier coefficient of the signal profile in the plane of crossing. To interpret the empirical results, a model consisting of differently oriented parallel sheets of cylinders was developed. Diffusion of water molecules inside and outside cylinders was simulated by the Monte Carlo method. Results Simulations predict that , agreeing with the empirical results, must increase with for large b-values, but may peak at a typical that depends on the thickness of the cylinder sheets for intermediate b-values. Thus, the thickness of axon layers in voxels with 2 predominant orientations can be detected from empirical taken at smaller b-values. Conclusion Based on the simulation results, recommendations are made on how to design a dMRI experiment with optimal b-value and range of in order to measure the thickness of axon sheets in the white matter, hence to distinguish (a) and (b).
    Date 2021-02-22
    Language en
    Library Catalog Wiley Online Library
    URL https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.28702
    Accessed 2/23/2021, 1:42:32 PM
    Rights © 2021 International Society for Magnetic Resonance in Medicine
    Extra _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.28702
    Volume 86
    Pages 429-441
    Publication Magnetic Resonance in Medicine
    DOI 10.1002/mrm.28702
    Issue 1
    Journal Abbr Magn. Reson. Med.
    ISSN 1522-2594
    Date Added 2/23/2021, 1:42:32 PM
    Modified 10/21/2022, 5:11:03 PM

    Tags:

    • Complexity

    Notes:

    Attachments

    • bmrmrsw21.pdf
  • Double-trace deformation in open quantum field theory

    Item Type Journal Article
    Author Xiangyi Meng
    Abstract The Keldysh formalism is capable of describing driven-dissipative dynamics of open quantum systems as nonunitary effective field theories that are not necessarily thermodynamical, thus often exhibiting new physics. Here, we introduce a general Keldysh action that maximally obeys Weinbergian constraints, including locality, Poincaré invariance, and two “CPT” constraints: complete positivity and trace preserving as well as charge, parity, and time reversal symmetry. We find that the perturbative Lindblad term responsible for driven-dissipative dynamics introduced therein has the natural form of a double-trace deformation O2, which, in the large N limit, possibly leads to a new nonthermal conformal fixed point. This fixed point is IR when Δ<d/2 or UV when Δ>d/2 given d the dimensions of spacetime and Δ the scaling dimension of O. Such a UV fixed point being not forbidden by Weinbergian constraints may suggest its existence and even completion of itself, in contrast to the common sense that dissipation effects are always IR relevant. This observation implies that driven-dissipative dynamics is much richer than thermodynamics, differing in not only its noncompliance with thermodynamic symmetry (e.g., the fluctuation-dissipation relation) but its UV/IR relevance as well. Examples including a (0+1)−d harmonic oscillator under continuous measurement and a (4−ε)−d classic O(N) vector model with quartic interactions are studied.
    Date 2021-07-20
    Library Catalog APS
    URL https://link.aps.org/doi/10.1103/PhysRevD.104.016016
    Accessed 2/22/2022, 8:28:27 PM
    Rights All rights reserved
    Extra Publisher: American Physical Society
    Volume 104
    Pages 016016
    Publication Physical Review D
    DOI 10.1103/PhysRevD.104.016016
    Issue 1
    Journal Abbr Phys. Rev. D
    Date Added 2/22/2022, 8:28:27 PM
    Modified 10/21/2022, 5:10:49 PM

    Tags:

    • Quantum network
    • Open quantum field theory

    Notes:

    Attachments

    • m21d.pdf
  • Entanglement-Structured LSTM Boosts Chaotic Time Series Forecasting

    Item Type Journal Article
    Author Xiangyi Meng
    Author Tong Yang
    Abstract Traditional machine-learning methods are inefficient in capturing chaos in nonlinear dynamical systems, especially when the time difference Δt between consecutive steps is so large that the extracted time series looks apparently random. Here, we introduce a new long-short-term-memory (LSTM)-based recurrent architecture by tensorizing the cell-state-to-state propagation therein, maintaining the long-term memory feature of LSTM, while simultaneously enhancing the learning of short-term nonlinear complexity. We stress that the global minima of training can be most efficiently reached by our tensor structure where all nonlinear terms, up to some polynomial order, are treated explicitly and weighted equally. The efficiency and generality of our architecture are systematically investigated and tested through theoretical analysis and experimental examinations. In our design, we have explicitly used two different many-body entanglement structures—matrix product states (MPS) and the multiscale entanglement renormalization ansatz (MERA)—as physics-inspired tensor decomposition techniques, from which we find that MERA generally performs better than MPS, hence conjecturing that the learnability of chaos is determined not only by the number of free parameters but also the tensor complexity—recognized as how entanglement entropy scales with varying matricization of the tensor.
    Date 2021-11-11
    Language en
    Library Catalog www.mdpi.com
    URL https://www.mdpi.com/1099-4300/23/11/1491
    Accessed 12/29/2021, 9:03:17 AM
    Rights http://creativecommons.org/licenses/by/3.0/
    Extra bibtex:lstm_my21
    Volume 23
    Pages 1491
    Publication Entropy
    DOI 10.3390/e23111491
    Issue 11
    Date Added 12/29/2021, 9:03:17 AM
    Modified 8/27/2023, 7:56:09 PM

    Tags:

    • Quantum network
    • Complexity

    Notes:

    Attachments

    • my21.pdf

      Contents

      • Introduction
      • Recurrent Architecture and Tensorization
        • Formalism of LSTM Architecture
        • Tensorized State Propagation
        • Many-Body Entanglement Structures
          • MPS
          • MERA
          • Scaling Behavior of EE
      • Theoretical Analysis
        • Expressive Power
        • Worst-Case Bound by EE
      • Results
        • Comparison of LSTM-Based Architectures
          • Lorenz System
          • Logistic Map
        • Comparison with Statistical/ML Models
        • Comparison with LSTM-MERA Alternatives
        • Generalization and Parameter Dependence of LSTM-MERA
          • Rössler System
          • Hénon Map
          • Duffing Oscillator System
          • Chirikov Standard Map
          • Real-World Data: Weather Forecasting
      • Discussion and Conclusions
      • Variants of LSTM-MERA
        • Translational Symmetry
        • Dilational Symmetry
        • Normalization/Unitarity
      • Common LSTM Architectures
        • HO- and HOT-RNN/LSTM
      • Preparation of Time Series Datasets
        • Discrete-Time Maps
        • Continuous-Time Dynamical Systems
        • Real-World Time Series: Weather
      • References
  • Fragmentation of outage clusters during the recovery of power distribution grids

    Item Type Journal Article
    Author Hao Wu
    Author Xiangyi Meng
    Author Michael M. Danziger
    Author Sean P. Cornelius
    Author Hui Tian
    Author Albert-László Barabási
    Abstract The understanding of recovery processes in power distribution grids is limited by the lack of realistic outage data, especially large-scale blackout datasets. By analyzing data from three electrical companies across the United States, we find that the recovery duration of an outage is connected with the downtime of its nearby outages and blackout intensity (defined as the peak number of outages during a blackout), but is independent of the number of customers affected. We present a cluster-based recovery framework to analytically characterize the dependence between outages, and interpret the dominant role blackout intensity plays in recovery. The recovery of blackouts is not random and has a universal pattern that is independent of the disruption cause, the post-disaster network structure, and the detailed repair strategy. Our study reveals that suppressing blackout intensity is a promising way to speed up restoration.
    Date 2022-11-30
    Language en
    Library Catalog www.nature.com
    URL https://www.nature.com/articles/s41467-022-35104-9
    Accessed 3/31/2023, 9:53:17 AM
    Rights 2022 The Author(s)
    Volume 13
    Pages 7372
    Publication Nature Communications
    DOI 10.1038/s41467-022-35104-9
    Issue 1
    Journal Abbr Nat Commun
    ISSN 2041-1723
    Date Added 3/31/2023, 9:53:17 AM
    Modified 10/24/2023, 9:16:16 PM

    Tags:

    • Statistical physics
    • Physics
    • thermodynamics and nonlinear dynamics

    Notes:

    Attachments

    • wmdctb22.pdf

      Contents

      • Fragmentation of outage clusters during the recovery of power distribution grids
        • Results
          • Factors affecting recovery duration
          • Cluster-based modeling of outage recovery
        • Discussion
        • Methods
          • Outage dataset
      • Definition of “blackouts
        • Outline placeholder
          • Cluster-based recovery process
        • Data availability
        • Code availability
        • References
        • Acknowledgements
        • Author contributions
        • Competing interests
        • Additional information
  • Minimal Evolution Time and Quantum Speed Limit of Non-Markovian Open Systems

    Item Type Journal Article
    Author Xiangyi Meng
    Author Chengjun Wu
    Author Hong Guo
    Date 2015-11-13
    Library Catalog CrossRef
    URL http://www.nature.com/articles/srep16357
    Accessed 11/13/2015, 9:23:44 PM
    Rights All rights reserved
    Extra Citation Key: q-speed-limit_mwg15
    Volume 5
    Pages 16357
    Publication Scientific Reports
    DOI 10.1038/srep16357
    Journal Abbr Sci. Rep.
    ISSN 2045-2322
    Date Added 11/13/2015, 9:23:44 PM
    Modified 9/21/2022, 9:18:58 PM

    Tags:

    • Quantum network
    • Complexity

    Notes:

    Attachments

    • mwg15.pdf

      Contents

      • Results
        • Geometric fidelity.
        • Minimal evolution time.
        • Quantum speed limit.
        • Non-Markovianity.
        • Renormalized Hamiltonian.
      • Discussion
      • Methods
        • Norms of operators.
        • Approximation for the dissipator of the damped Jaynes-Cummings model in the strong-coupling regime.
      • Acknowledgements
      • Author Contributions
      • Figure 1.  Solutions of the population of the damped Jaynes-Cummings model13 in the weak- (black line) and strong-coupling regime (red line), with γ0 = 0.
      • Figure 2.  Minimal evolution time (red solid line) of the same model and its different QSL bounds (black lines) as a function of γ0.
      • Figure 3.  Numerical solution of the relative-purity fidelity (red lines) and the dissipator (blue lines) of the quantum dot model35.
  • Multiple-scale perturbation method on integro-differential equations: Application to continuous-time quantum walks on regular networks in non-Markovian reservoirs

    Item Type Journal Article
    Author Xiangyi Meng
    Author Yang Li
    Author Jian-Wei Zhang
    Author Hong Guo
    Author H. Eugene Stanley
    Abstract Non-Markovianity may significantly speed up quantum dynamics when the system interacts strongly with an infinite large reservoir, of which the coupling spectrum should be fine-tuned. The potential benefits are evident in many dynamics schemes, especially the continuous-time quantum walk. Difficulty exists, however, in producing closed-form solutions with controllable accuracy against the complexity of memory kernels. Here, we introduce a new multiple-scale perturbation method that works on integro-differential equations for general study of memory effects in dynamical systems. We propose an open-system model in which a continuous-time quantum walk is enclosed in a non-Markovian reservoir, that naturally corresponds to an error correction algorithm scheme. By applying the multiple-scale method we show how emergence of different timescales is related to transition of system dynamics into the non-Markovian regime. We find that up to two long-term modes and two short-term modes exist in regular networks, limited by their intrinsic symmetries. In addition to the effective approximation by our perturbation method on general forms of reservoirs, the speed-up of quantum walks assisted by non-Markovianity is also confirmed, revealing the advantage of reservoir engineering in designing time-sensitive quantum algorithms.
    Date 2019-09-19
    Short Title Multiple-scale perturbation method on integro-differential equations
    Library Catalog APS
    URL https://link.aps.org/doi/10.1103/PhysRevResearch.1.023020
    Accessed 9/22/2019, 8:44:45 AM
    Rights All rights reserved
    Extra Citation Key: multi-scale_mlzgs19
    Volume 1
    Pages 023020
    Publication Physical Review Research
    DOI 10.1103/PhysRevResearch.1.023020
    Issue 2
    Journal Abbr Phys. Rev. Research
    Date Added 9/22/2019, 8:44:45 AM
    Modified 10/21/2022, 5:11:28 PM

    Tags:

    • Quantum network

    Notes:

    Attachments

    • mlzgs19.pdf
  • Peak fraction of infected in epidemic spreading for multi-community networks

    Item Type Journal Article
    Author Jing Ma
    Author Xiangyi Meng
    Author Lidia A Braunstein
    Abstract One of the most effective strategies to mitigate the global spreading of a pandemic (e.g. coronavirus disease 2019) is to shut down international airports. From a network theory perspective, this is since international airports and flights, essentially playing the roles of bridge nodes and bridge links between countries as individual communities, dominate the epidemic spreading characteristics in the whole multi-community system. Among all epidemic characteristics, the peak fraction of infected, $I_{\max}$, is a decisive factor in evaluating an epidemic strategy given limited capacity of medical resources but is seldom considered in multi-community models. In this article, we study a general two-community system interconnected by a fraction $r$ of bridge nodes and its dynamic properties, especially $I_{\max}$, under the evolution of the susceptible-infected-recovered model. Comparing the characteristic time scales of different parts of the system allows us to analytically derive the asymptotic behaviour of $I_{\max}$ with $r$, as $r\rightarrow 0$, which follows different power-law relations in each regime of the phase diagram. We also detect crossovers when $I_{\max}$ changes from one power law to another, crossing different power-law regimes as driven by $r$. Our results enable a better prediction of the effectiveness of strategies acting on bridge nodes, denoted by the power-law exponent $\epsilon_I$ as in $I_{\max}\propto r^{1/\epsilon_I}$.
    Date 2022-06-01
    Library Catalog Silverchair
    URL https://doi.org/10.1093/comnet/cnac021
    Accessed 7/15/2022, 5:19:22 PM
    Rights All rights reserved
    Extra bibtex:crossover-sir-peak_mmb22
    Volume 10
    Pages cnac021
    Publication Journal of Complex Networks
    DOI 10.1093/comnet/cnac021
    Issue 3
    Journal Abbr J. Complex Netw.
    ISSN 2051-1329
    Date Added 7/15/2022, 5:19:22 PM
    Modified 10/21/2022, 5:08:26 PM

    Tags:

    • Network of network

    Notes:

    Attachments

    • mmb22.pdf
  • Percolation Theories for Quantum Networks

    Item Type Journal Article
    Author Xiangyi Meng
    Author Xinqi Hu
    Author Yu Tian
    Author Gaogao Dong
    Author Renaud Lambiotte
    Author Jianxi Gao
    Author Shlomo Havlin
    Abstract Quantum networks have experienced rapid advancements in both theoretical and experimental domains over the last decade, making it increasingly important to understand their large-scale features from the viewpoint of statistical physics. This review paper discusses a fundamental question: how can entanglement be effectively and indirectly (e.g., through intermediate nodes) distributed between distant nodes in an imperfect quantum network, where the connections are only partially entangled and subject to quantum noise? We survey recent studies addressing this issue by drawing exact or approximate mappings to percolation theory, a branch of statistical physics centered on network connectivity. Notably, we show that the classical percolation frameworks do not uniquely define the network’s indirect connectivity. This realization leads to the emergence of an alternative theory called “concurrence percolation”, which uncovers a previously unrecognized quantum advantage that emerges at large scales, suggesting that quantum networks are more resilient than initially assumed within classical percolation contexts, offering refreshing insights into future quantum network design.
    Date 2023-11
    Language en
    Library Catalog www.mdpi.com
    URL https://www.mdpi.com/1099-4300/25/11/1564
    Accessed 11/20/2023, 10:00:28 AM
    Rights http://creativecommons.org/licenses/by/3.0/
    Extra Number: 11 Publisher: Multidisciplinary Digital Publishing Institute
    Volume 25
    Pages 1564
    Publication Entropy
    DOI 10.3390/e25111564
    Issue 11
    ISSN 1099-4300
    Date Added 11/20/2023, 10:00:28 AM
    Modified 11/20/2023, 10:00:28 AM

    Tags:

    • percolation
    • critical phenomena
    • entanglement distribution
    • hypergraph
    • networks of networks
    • quantum network

    Notes:

    Attachments

    • mhtdlgh23.pdf

      Contents

      • Introduction
      • Quantum Networks (QN)
        • Qudit-Based Quantum Networks
        • Quantum Networks Are the Basis of Tensor Networks
        • Multipartite Quantum Networks
      • Percolation of Complex Network
        • Percolation of Single-Layer Networks
        • Percolation of Networks of Networks
        • Percolation of Hypergraphs
      • Classical Percolation in Quantum Networks
        • Classical Entanglement Percolation (CEP)
        • Quantum Entanglement Percolation (QEP)
      • Concurrence Percolation in Quantum Networks
        • No Need to Establish Singlets
        • Deterministic Entanglement Transmission (DET)
        • Concurrence Percolation Theory
        • Results
      • Algorithms
        • Identification of Series–Parallel Networks
        • Star-Mesh Transform
        • Fast Numerical Approximation for Concurrence Percolation
          • Parallel Approximation
          • Sm Approximation
          • Results
      • Discussion and Conclusions
      • References
  • Power-law distribution of degree–degree distance: A better representation of the scale-free property of complex networks

    Item Type Journal Article
    Author Bin Zhou
    Author Xiangyi Meng
    Author H. Eugene Stanley
    Date 2020-06-30
    Short Title Power-law distribution of degree–degree distance
    Library Catalog pnas.org (Atypon)
    URL https://www.pnas.org/doi/10.1073/pnas.1918901117
    Accessed 7/12/2022, 12:08:33 AM
    Rights All rights reserved
    Extra bibtex: degree-degree-distance_zms20
    Volume 117
    Pages 14812-14818
    Publication Proceedings of the National Academy of Sciences
    DOI 10.1073/pnas.1918901117
    Issue 26
    Journal Abbr Proc. Natl. Acad. Sci. U.S.A.
    Date Added 7/12/2022, 12:08:33 AM
    Modified 9/21/2022, 9:18:36 PM

    Tags:

    • Complexity

    Notes:

    Attachments

    • zms20.pdf
  • Quantum Brownian motion model for the stock market

    Item Type Journal Article
    Author Xiangyi Meng
    Author Jian-Wei Zhang
    Author Hong Guo
    Abstract It is believed by the majority today that the efficient market hypothesis is imperfect because of market irrationality. Using the physical concepts and mathematical structures of quantum mechanics, we construct an econophysical framework for the stock market, based on which we analogously map massive numbers of single stocks into a reservoir consisting of many quantum harmonic oscillators and their stock index into a typical quantum open system—a quantum Brownian particle. In particular, the irrationality of stock transactions is quantitatively considered as the Planck constant within Heisenberg’s uncertainty relationship of quantum mechanics in an analogous manner. We analyze real stock data of Shanghai Stock Exchange of China and investigate fat-tail phenomena and non-Markovian behaviors of the stock index with the assistance of the quantum Brownian motion model, thereby interpreting and studying the limitations of the classical Brownian motion model for the efficient market hypothesis from a new perspective of quantum open system dynamics.
    Date 2016-06-15
    Library Catalog ScienceDirect
    URL http://www.sciencedirect.com/science/article/pii/S0378437116001928
    Accessed 4/26/2019, 3:57:56 PM
    Rights All rights reserved
    Volume 452
    Pages 281-288
    Publication Physica A: Statistical Mechanics and its Applications
    DOI 10.1016/j.physa.2016.02.026
    Journal Abbr Physica A
    ISSN 0378-4371
    Date Added 4/26/2019, 3:57:56 PM
    Modified 10/21/2022, 5:11:45 PM

    Tags:

    • Complexity

    Notes:

    Attachments

    • mzg16.pdf
  • Quantum spatial-periodic harmonic model for daily price-limited stock markets

    Item Type Journal Article
    Author Xiangyi Meng
    Author Jian-Wei Zhang
    Author Jingjing Xu
    Author Hong Guo
    Abstract We investigate the behaviors of stocks in daily price-limited stock markets by purposing a quantum spatial-periodic harmonic model. The stock price is considered to be oscillating and damping in a quantum spatial-periodic harmonic oscillator potential well. A complicated non-linear relation including inter-band positive correlation and intra-band negative correlation between the volatility and trading volume of a stock is numerically derived with the energy band structure of the model concerned. The effectiveness of price limit is re-examined, with some observed characteristics of price-limited stock markets in China studied by applying our quantum model.
    Date 2015-11-15
    Short Title Quantum spatial-periodic harmonic model for daily price-limited stock markets
    URL http://www.sciencedirect.com/science/article/pii/S0378437115006019
    Rights All rights reserved
    Volume 438
    Pages 154-160
    Publication Physica A: Statistical Mechanics and its Applications
    DOI 10.1016/j.physa.2015.06.041
    Journal Abbr Physica A
    ISSN 0378-4371
    Date Added 11/3/2015, 7:37:55 PM
    Modified 10/21/2022, 5:12:10 PM

    Tags:

    • Complexity

    Notes:

    Attachments

    • mzxg15.pdf

      Contents

      • Introduction
      • Quantum spatial-periodic harmonic oscillator potential well
      • Influence of daily price limit on stock markets
      • ``Quantum'' phenomena of price-limited stock markets
      • Discussion and conclusion
      • Acknowledgments
      • References
  • Realistic modelling of information spread using peer-to-peer diffusion patterns

    Item Type Journal Article
    Author Bin Zhou
    Author Sen Pei
    Author Lev Muchnik
    Author Xiangyi Meng
    Author Xiaoke Xu
    Author Alon Sela
    Author Shlomo Havlin
    Author H. Eugene Stanley
    Abstract In computational social science, epidemic-inspired spread models have been widely used to simulate information diffusion. However, recent empirical studies suggest that simple epidemic-like models typically fail to generate the structure of real-world diffusion trees. Such discrepancy calls for a better understanding of how information spreads from person to person in real-world social networks. Here, we analyse comprehensive diffusion records and associated social networks in three distinct online social platforms. We find that the diffusion probability along a social tie follows a power-law relationship with the numbers of disseminator’s followers and receiver’s followees. To develop a more realistic model of information diffusion, we incorporate this finding together with a heterogeneous response time into a cascade model. After adjusting for observational bias, the proposed model reproduces key structural features of real-world diffusion trees across the three platforms. Our finding provides a practical approach to designing more realistic generative models of information diffusion.
    Date 2020-08-28
    Language en
    Library Catalog www.nature.com
    URL https://www.nature.com/articles/s41562-020-00945-1
    Accessed 9/7/2022, 8:42:56 PM
    Rights 2020 The Author(s), under exclusive licence to Springer Nature Limited
    Extra Number: 11 Publisher: Nature Publishing Group
    Volume 4
    Pages 1198-1207
    Publication Nature Human Behaviour
    DOI 10.1038/s41562-020-00945-1
    Issue 11
    Journal Abbr Nat. Hum. Behav.
    ISSN 2397-3374
    Date Added 9/7/2022, 8:42:56 PM
    Modified 10/21/2022, 5:09:14 PM

    Tags:

    • Complexity

    Notes:

    Attachments

    • zpmmxshs20.pdf

      Contents

      • Results
        • Information diffusion in real-world social platforms.
        • Observed peer-to-peer information diffusion.
        • Adjusting observational bias.
        • Patterns of peer-to-peer information diffusion.
        • Realistic modelling of information diffusion.
        • Validation of the observational bias correction using LiveJournal data.
      • Discussion
      • Methods
        • Data
        • Estimation of adjustment factor
        • Model simulations
        • Reporting Summary
      • Acknowledgements
      • Fig. 1 The unadjusted diffusion probability following (kd,kr) links.
      • Fig. 2 The power-law relationship between peer-to-peer diffusion probability and users’ degrees.
      • Fig. 3 The power-law distributions of response time τ.
      • Fig. 4 Comparison of distributions of diffusion tree size N, depth L and structural virality D.
      • Fig. 5 Distributions of the lifetime T of diffusion trees.
      • Fig. 6 A consistency check for the observational bias correction using LiveJournal data.
  • Robustness of interdependent scale-free networks based on link addition strategies

    Item Type Journal Article
    Author Chao-Yang Chen
    Author Yang Zhao
    Author Huanmei Qin
    Author Xiangyi Meng
    Author Jianxi Gao
    Abstract It is well known that interdependent networks are more vulnerable to cascading failure than single and isolated networks. In this report, we propose a new scheme to improve the robustness of interdependent scale-free network under degree-based deliberate attacks by adding links to enhance the connectivity of the interdependent network. Our proposal details 14 link addition strategies using two link importance functions. To verify the feasibility of the proposed strategies, we synthesize three different types of two-layer interdependent Barabási–Albert networks where nodes from each layer are, after ranked by their degrees, bijectively inter-coupled by assortative coupling (AC), disassortative coupling (DC), or random coupling (RC). We find that when the number of attacked nodes in the system is small, the harmonic closeness (HS-IDD) link addition strategy has the highest efficiency. Among them, S indicates that the information fusion method of each layer of the network is addition, IDD refers to the degree difference of network. With the increase of the attack proportion, the degree (DS-IDD) link addition efficiency gradually increases, and the effect is more obvious in DC and RC. Besides, through comparing different strategies, under DC and RC, the link addition strategy for the product is more effective than the sum-based strategy. However, under AC, this phenomenon is not obvious. The results show that our proposed approach can enhance the robustness of interdependent networks, and that our method provides a valuable reference for the control and prevention of cascading failures in existing interdependent networks.
    Date 2022-10-15
    Language en
    Library Catalog ScienceDirect
    URL https://www.sciencedirect.com/science/article/pii/S0378437122005520
    Accessed 9/7/2022, 8:41:57 PM
    Rights All rights reserved
    Volume 604
    Pages 127851
    Publication Physica A: Statistical Mechanics and its Applications
    DOI 10.1016/j.physa.2022.127851
    Journal Abbr Physica A
    ISSN 0378-4371
    Date Added 9/7/2022, 8:41:57 PM
    Modified 10/21/2022, 5:08:29 PM

    Tags:

    • Network of network

    Notes:

    Attachments

    • czqmg22.pdf

      Contents

      • Introduction
      • Methods
        • Interdependent network model
        • Cascading failure model
        • Connectivity link addition strategies
          • Edge addition strategy for single layer network
          • Interdependent network edge addition strategy using IDD
          • Link adding strategy using IDD, degree, betweenness and harmonic closeness
      • Results
      • Discussion
      • CRediT authorship contribution statement
      • Declaration of competing interest
      • Acknowledgments
      • References
  • Scale-free networks beyond power-law degree distribution

    Item Type Journal Article
    Author Xiangyi Meng
    Author Bin Zhou
    Abstract Complex networks across various fields are often considered to be scale free—a statistical property usually solely characterized by a power-law distribution of the nodes’ degree k. However, this characterization is incomplete. In real-world networks, the distribution of the degree–degree distance η, a simple link-based metric of network connectivity similar to k, appears to exhibit a stronger power-law distribution than k. While offering an alternative characterization of scale-freeness, the discovery of η raises a fundamental question: do the power laws of k and η represent the same scale-freeness? To address this question, here we investigate the exact asymptotic relationship between the distributions of k and η, proving that every network with a power-law distribution of k also has a power-law distribution of η, but not vice versa. This prompts us to introduce two network models as counterexamples that have a power-law distribution of η but not k, constructed using the preferential attachment and fitness mechanisms, respectively. Both models show promising accuracy by fitting only one model parameter each when modeling real-world networks. Our findings suggest that η is a more suitable indicator of scale-freeness and can provide a deeper understanding of the universality and underlying mechanisms of scale-free networks.
    Date 2023-11-01
    Library Catalog ScienceDirect
    URL https://www.sciencedirect.com/science/article/pii/S0960077923010755
    Accessed 10/24/2023, 9:13:22 PM
    Rights All rights reserved
    Extra bibtex:scale-free_mz23
    Volume 176
    Pages 114173
    Publication Chaos, Solitons & Fractals
    DOI 10.1016/j.chaos.2023.114173
    Journal Abbr Chaos Solitons Fractals
    ISSN 0960-0779
    Date Added 10/24/2023, 9:13:22 PM
    Modified 10/27/2023, 2:07:05 AM

    Tags:

    • Complex networks
    • Degree distribution
    • Degree–degree distance distribution
    • Power-law
    • Scale-free

    Notes:

    Attachments

    • mz23.pdf

      Contents

      • Introduction
      • Results
        • Preferential attachment of internal links
        • Fitness with threshold
        • The Erdos–Renyi model revisited
      • Discussion and Conclusions
      • CRediT authorship contribution statement
      • Declaration of competing interest
      • Data availability
      • Acknowledgments
      • Appendix A. Supplementary data
      • References
  • The nature and nurture of network evolution

    Item Type Journal Article
    Author Bin Zhou
    Author Petter Holme
    Author Zaiwu Gong
    Author Choujun Zhan
    Author Yao Huang
    Author Xin Lu
    Author Xiangyi Meng
    Abstract Although the origin of the fat-tail characteristic of the degree distribution in complex networks has been extensively researched, the underlying cause of the degree distribution characteristic across the complete range of degrees remains obscure. Here, we propose an evolution model that incorporates only two factors: the node’s weight, reflecting its innate attractiveness (nature), and the node’s degree, reflecting the external influences (nurture). The proposed model provides a good fit for degree distributions and degree ratio distributions of numerous real-world networks and reproduces their evolution processes. Our results indicate that the nurture factor plays a dominant role in the evolution of social networks. In contrast, the nature factor plays a dominant role in the evolution of non-social networks, suggesting that whether nodes are people determines the dominant factor influencing the evolution of real-world networks.
    Date 2023-11-03
    Language en
    Library Catalog www.nature.com
    URL https://www.nature.com/articles/s41467-023-42856-5
    Accessed 11/20/2023, 10:01:12 AM
    Rights 2023 The Author(s)
    Extra Number: 1 Publisher: Nature Publishing Group
    Volume 14
    Pages 7031
    Publication Nature Communications
    DOI 10.1038/s41467-023-42856-5
    Issue 1
    Journal Abbr Nat Commun
    ISSN 2041-1723
    Date Added 11/20/2023, 10:01:12 AM
    Modified 11/20/2023, 10:01:12 AM

    Tags:

    • Complex networks
    • Nonlinear phenomena

    Notes:

    Attachments

    • zhgzhlm23.pdf

      Contents

      • Results
        • Nature–nurture’model
        • Validation
      • Discussion
      • Methods
        • Degree and degree-ratio distributions of the nature–nurture’model
        • Reporting summary
      • Data availability
      • Code availability
      • References
      • Acknowledgements
      • Author contributions
      • Competing interests
      • Additional information